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Glama
tensakulabs

mem0-mcp

Official
by tensakulabs
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Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
MEM0_USER_IDNoUser ID for memory filteringjustin
MEM0_API_BASENoOpenMemory API (for writes)http://127.0.0.1:8765
MEM0_NEO4J_URLNoNeo4j Bolt endpointbolt://127.0.0.1:7687
MEM0_COLLECTIONNoQdrant collection nameopenmemory
MEM0_NEO4J_USERNoNeo4j usernameneo4j
MEM0_OLLAMA_URLNoOllama (for embeddings)http://127.0.0.1:11435
MEM0_QDRANT_URLNoQdrant REST APIhttp://127.0.0.1:6333
MEM0_EMBED_MODELNoEmbedding model namenomic-embed-text:latest
MEM0_NEO4J_PASSWORDNoNeo4j passwordmem0graph

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
search_memoriesA

Semantically search memories for relevant context.

Use this at the start of tasks to recall relevant preferences,
decisions, patterns, and facts from previous sessions.

Args:
    query: Natural language search query (e.g., "TypeScript preferences",
           "server architecture", "coding style")
add_memoryA

Store a new memory for future recall.

Use this to remember important facts, user preferences, architectural
decisions, project context, and lessons learned.

Args:
    text: The fact or information to remember (e.g., "Justin prefers
          TypeScript over Python for new projects")
list_memoriesA

List all stored memories for the current user.

Returns all memories from both Arc and Atlas in the shared store.

delete_memoryA

Delete a specific memory by its ID.

Args:
    memory_id: The full UUID of the memory to delete
search_graphA

Search the knowledge graph for entity relationships.

Finds entities matching the query and their connections.
Use this to understand relationships between people, projects,
technologies, and concepts.

Args:
    query: Entity or topic to search for (e.g., "Justin", "OpenClaw",
           "Hetzner server")
get_entityA

Get all relationships for a specific entity.

Returns both incoming and outgoing connections.

Args:
    name: The entity name (e.g., "Justin", "TypeScript", "Hetzner")

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.1/5.0

Scored across 6 tools

Disambiguation4/5

Tools are mostly distinct: add, delete, list, search (semantic and graph) each target different operations. However, search_memories and search_graph could be confused if descriptions aren't carefully read, as both involve searching.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., add_memory, delete_memory, list_memories, search_memories, get_entity, search_graph). No mixed conventions.

Tool Count5/5

With 6 tools, the set is well-scoped for a memory management server. It covers essential operations without being too few or too many.

Completeness4/5

The tool surface includes create, read, delete, and search operations. A notable gap is the lack of an update_memory tool, but the set is otherwise functional for the domain.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources